Fluid classification is essential to downhole fluid analysis and is often the first step in workflows for quantitative fluid characterization during formation sampling and testing. Conventional fluid classifiers deployed in formation testers generally use physical bulk fluid properties and compositional measurement data to classify different types of downhole fluids. The compositional data used for the classification is based on measurements from downhole optical sensors, which may require complex calibration to ensure data reliability. However, the complexity of sensor-based calibration is often difficult to manage given the number of permutations of physical and chemical properties that are typically measured for fluid classification purposes. Further, the accuracy of fluid classifications that are directly based on sensor measurements may be limited by the sensitivity of the optical sensors used to detect the different fluid types downhole. For example, the optical sensors of a conventional fluid classifier may be overly sensitive to certain parameters that cause noisy data in real-world systems. While the use of multiple sensors from various downhole tools may improve the quality of the fluid classification, the availability of useful sensor data and the costs of sensor calibration may limit or discourage the use of such a multi-disciplinary approach in many oilfield applications.